Reducing the Local Bias in Calibrating the General COCOMO.

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Presentation transcript:

Reducing the Local Bias in Calibrating the General COCOMO

Outline Local bias is a threat to general calibration. How to deal with it? What are the results of our solution?

Local calibration is widely used to enhance estimation performance. General Estimation Model Local Estimation Model (Organization 1) Local Estimation Model (Organization 2) Local Estimation Model (Organization 3)

Local data are combined to calibrate general estimation model. Organization 1 Organization 2 Organization 3 General Model

Localized data distributions Increase the fitting error of general calibration. Decrease the estimation performance of general model. Reflect the local bias among organizations.

Outline Local bias is a threat to general calibration. How to deal with it? What are the results of our solution?

Introduce another factor to account for the local bias in the data. This definition embodies the intuition that the more the local constants deviate from the general constants, the greater local bias is. When Estimate_Local is greater (less) than Estimate_General, Bias is larger (smaller) than 1.

Additional steps before general calibration Since Bias is determined before calibration, it is combined with actual effort to normalize the equation. If A_Local and B_Local are not directly available, they are generated by local calibration. If A_General and B_General are not directly available, they are generated by general calibration.

Two experiments show the effect of combining Bias into actual effort. COCOM O Data General Calibration Using All Data General Calibration Using Cross Validation Combining Bias into Actual Effort (Dataset P) Doing Nothing to the Data (Dataset R) COCOMO NASA & COCOMO 2000

Outline Local bias is a threat to general calibration. How to deal with it? What are the results of our solution?

Fitting Results Using All Data Cross Validation Results

What do the results tell us? In both experiments, the calibration errors of dataset P are slightly lower than those of dataset R, which indicates that there is slight local bias in the raw dataset. Proper adjustment on raw data can help in reducing the local bias and improve model performance.